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000 | 11528naa a2203037 4500 | |
001 | oai:DiVA.org:kth-302811 | |
003 | SwePub | |
008 | 211001s2020 | |||||||||||000 ||eng| | |
009 | oai:lup.lub.lu.se:87b1824a-253f-4c1b-9fad-cf5f0f5b6741 | |
024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3028112 URI |
024 | 7 | a https://doi.org/10.1038/s41586-020-2896-22 DOI |
024 | 7 | a https://lup.lub.lu.se/record/87b1824a-253f-4c1b-9fad-cf5f0f5b67412 URI |
040 | a (SwePub)kthd (SwePub)lu | |
041 | a engb eng | |
042 | 9 SwePub | |
072 | 7 | a ref2 swepub-contenttype |
072 | 7 | a art2 swepub-publicationtype |
100 | 1 | a Bar, N.u Weizmann Institute of Science Israel4 aut |
245 | 1 0 | a A reference map of potential determinants for the human serum metabolome |
264 | c 2020-11-11 | |
264 | 1 | b Nature Research,c 2020 |
338 | a print2 rdacarrier | |
500 | a QC 20211001 | |
520 | a The serum metabolome contains a plethora of biomarkers and causative agents of various diseases, some of which are endogenously produced and some that have been taken up from the environment1. The origins of specific compounds are known, including metabolites that are highly heritable2,3, or those that are influenced by the gut microbiome4, by lifestyle choices such as smoking5, or by diet6. However, the key determinants of most metabolites are still poorly understood. Here we measured the levels of 1,251 metabolites in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals. We applied machine-learning algorithms to predict metabolite levels in held-out individuals on the basis of host genetics, gut microbiome, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically significant predictions for more than 76% of the profiled metabolites. Diet and microbiome had the strongest predictive power, and each explained hundreds of metabolites—in some cases, explaining more than 50% of the observed variance. We further validated microbiome-related predictions by showing a high replication rate in two geographically independent cohorts7,8 that were not available to us when we trained the algorithms. We used feature attribution analysis9 to reveal specific dietary and bacterial interactions. We further demonstrate that some of these interactions might be causal, as some metabolites that we predicted to be positively associated with bread were found to increase after a randomized clinical trial of bread intervention. Overall, our results reveal potential determinants of more than 800 metabolites, paving the way towards a mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating the levels of circulating metabolites. | |
650 | 7 | a MEDICIN OCH HÄLSOVETENSKAPx Hälsovetenskapx Näringslära0 (SwePub)303042 hsv//swe |
650 | 7 | a MEDICAL AND HEALTH SCIENCESx Health Sciencesx Nutrition and Dietetics0 (SwePub)303042 hsv//eng |
653 | a biomarker | |
653 | a digestive system | |
653 | a map | |
653 | a metabolism | |
653 | a metabolite | |
653 | a serum | |
653 | a adult | |
653 | a aged | |
653 | a algorithm | |
653 | a anthropometry | |
653 | a Article | |
653 | a blood analysis | |
653 | a cohort analysis | |
653 | a controlled study | |
653 | a diet | |
653 | a genetics | |
653 | a human | |
653 | a human experiment | |
653 | a intestine flora | |
653 | a lifestyle | |
653 | a machine learning | |
653 | a metabolomics | |
653 | a normal human | |
653 | a phenotype | |
653 | a prediction | |
653 | a priority journal | |
653 | a bread | |
653 | a female | |
653 | a male | |
653 | a metabolome | |
653 | a middle aged | |
653 | a nonalcoholic fatty liver | |
653 | a physiology | |
653 | a randomized controlled trial | |
653 | a reproducibility | |
653 | a season | |
653 | a standard | |
653 | a Bacteria (microorganisms) | |
653 | a oxygenase | |
653 | a PHYHD1A protein | |
653 | a human | |
653 | a Cohort Studies | |
653 | a Gastrointestinal Microbiome | |
653 | a Healthy Volunteers | |
653 | a Humans | |
653 | a Life Style | |
653 | a Non-alcoholic Fatty Liver Disease | |
653 | a Oxygenases | |
653 | a Reference Standards | |
653 | a Reproducibility of Results | |
653 | a Seasons | |
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700 | 1 | a Franks, Paulu Lund University,Lunds universitet,Genetisk och molekylär epidemiologi,Forskargrupper vid Lunds universitet,Genetic and Molecular Epidemiology,Lund University Research Groups4 aut0 (Swepub:lu)med-plf |
700 | 1 | a Pedersen, O.4 aut |
700 | 1 | a Segal, E.u Weizmann Institute of Science Israel4 aut |
700 | 1 | a consortium, The IMI DIRECT4 aut |
710 | 2 | a Weizmann Institute of Science Israelb Science for Life Laboratory, SciLifeLab4 org |
773 | 0 | t Natured : Nature Researchg 588:7836, s. 135-140q 588:7836<135-140x 0028-0836x 1476-4687 |
856 | 4 | u https://push-zb.helmholtz-muenchen.de/deliver.php?id=28940 |
856 | 4 | u https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095943937&doi=10.1038%2fs41586-020-2896-2&partnerID=40&md5=6e0c85be4e191e6ca3ff29a21e193fc8y FULLTEXT |
856 | 4 | u http://dx.doi.org/10.1038/s41586-020-2896-2y FULLTEXT |
856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302811 |
856 | 4 8 | u https://doi.org/10.1038/s41586-020-2896-2 |
856 | 4 8 | u https://lup.lub.lu.se/record/87b1824a-253f-4c1b-9fad-cf5f0f5b6741 |
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